Mesoscale Reconstruction of Images and Networks Using Tensor Decomposition

Explore mesoscale reconstruction techniques for images and networks using tensor decomposition in this 50-minute lecture by Hanbaek Lyu from the University of Wisconsin-Madison. Delve into a unified framework that utilizes low-rank mesoscale structures, examining how global reconstruction error relates to mesoscale reconstruction. Discover the application of online CP-dictionary learning for multi-modal datasets, which incorporates CP tensor decomposition to efficiently represent inter-modal relationships. Learn about convergence guarantees and computational advantages of these algorithms. The lecture covers topics such as motivation, examples of networks, traditional pipelines, data-driven approaches, low-rank structures, random product graphs, network sampling, and dictionary learning for networks.

Intro
Collaborators
Motivation
Examples of networks
Traditional pipeline
Datadriven approach
Networks
Low Rank
Random Product Graph
Network sampling
Algorithm
Dictionary learning
Dictionary learning networks
Algorithms